Current Issue

2020 Vol. 9, No. 5
Special Topic Papers: Radar Imaging, Target Recognition and Countermeasure
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars. Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed. At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios. Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.

An important issue in a Synthetic Aperture Radar (SAR) system employing Multidimensional Waveform Encoding (MWE) is the fulfillments of Digital BeamForming (DBF) on receive in elevation for a reliable separation of the mutually overlapped echoes from multiple transmit waveforms. In this paper, the performance of a separation approach employing hybrid DBF in elevation by combining the onboard real-time beam-steering and a posteriori null-steering DBF on the ground is elaborately investigated. As a cascaded structure which comprises two subsequent DBF networks, the onboard part effectuates the steering of the mainlobes within multiple partitioned groups of antenna elements to ensure sufficient signal receive gain over the whole swath; the a posteriori adaptive DBF network on the ground mainly performs the task of placing nulls to cancel the range interference from other transmit waveforms, which enables adaptive beamforming to avoid the topographic height variation problem. Two type of onboard realtime beamformers are investigated, depending on the utilization of the transmit waveform structure information or not. The performance of the hybrid DBF approach is theoretically analyzed and evaluated in simulation experiment. It is shown that the hybrid DBF approach can provide additional dimensions of the trade-space to optimize the performance on range ambiguity suppression and signal-to-noise ratio improvement, as well as the onboard data volume reduction. In comparison with the a posteriori DBF on the ground, employing the hybrid DBF networks can get satisfactory performance while remarkably reducing the output data volume, in the presented example, the corresponding output channel number is decreased from 10 to 6.

An important issue in a Synthetic Aperture Radar (SAR) system employing Multidimensional Waveform Encoding (MWE) is the fulfillments of Digital BeamForming (DBF) on receive in elevation for a reliable separation of the mutually overlapped echoes from multiple transmit waveforms. In this paper, the performance of a separation approach employing hybrid DBF in elevation by combining the onboard real-time beam-steering and a posteriori null-steering DBF on the ground is elaborately investigated. As a cascaded structure which comprises two subsequent DBF networks, the onboard part effectuates the steering of the mainlobes within multiple partitioned groups of antenna elements to ensure sufficient signal receive gain over the whole swath; the a posteriori adaptive DBF network on the ground mainly performs the task of placing nulls to cancel the range interference from other transmit waveforms, which enables adaptive beamforming to avoid the topographic height variation problem. Two type of onboard realtime beamformers are investigated, depending on the utilization of the transmit waveform structure information or not. The performance of the hybrid DBF approach is theoretically analyzed and evaluated in simulation experiment. It is shown that the hybrid DBF approach can provide additional dimensions of the trade-space to optimize the performance on range ambiguity suppression and signal-to-noise ratio improvement, as well as the onboard data volume reduction. In comparison with the a posteriori DBF on the ground, employing the hybrid DBF networks can get satisfactory performance while remarkably reducing the output data volume, in the presented example, the corresponding output channel number is decreased from 10 to 6.

In the Medium-Earth-Orbit Synthetic Aperture Radar (MEO SAR), the curved trajectory and long synthetic aperture time lead to a two-dimensional spatial variation in the signals. Traditional methods usually process the range and azimuth variations separately, and the computational complexities are high. Herein, we study the Doppler rate distribution across a large scene and propose a non-orthogonal and nonlinear coordinate system wherein the MEO SAR signals satisfy the azimuth-shift–invariant property. Thus, the efficiency of the image formation processor can be significantly improved. The higher-order Doppler parameters are addressed by the Doppler linearization. Then, more precise focusing can be achieved, and the azimuth time-shift caused by the changes in signal distribution is addressed. Finally, the processing results of simulated stripmap-mode data with a 2-m resolution are presented to validate the effectiveness of the proposed algorithm. In the Medium-Earth-Orbit Synthetic Aperture Radar (MEO SAR), the curved trajectory and long synthetic aperture time lead to a two-dimensional spatial variation in the signals. Traditional methods usually process the range and azimuth variations separately, and the computational complexities are high. Herein, we study the Doppler rate distribution across a large scene and propose a non-orthogonal and nonlinear coordinate system wherein the MEO SAR signals satisfy the azimuth-shift–invariant property. Thus, the efficiency of the image formation processor can be significantly improved. The higher-order Doppler parameters are addressed by the Doppler linearization. Then, more precise focusing can be achieved, and the azimuth time-shift caused by the changes in signal distribution is addressed. Finally, the processing results of simulated stripmap-mode data with a 2-m resolution are presented to validate the effectiveness of the proposed algorithm.
Independent processing of each polarization channel and three-dimensional multistage imaging ignore the correlation between data, resulting in the mismatch between scattering centers and the inaccurate acquisition of polarization scattering matrices. To address these issues, a full-polarization Synthetic Aperture Radar (SAR) joint multidimensional reconstruction method based on sparse reconstruction is proposed in this study. In this method, all polarization channels and dimensions are integrated by setting the joint sparse constraints, and the full-polarization SAR joint multidimensional reconstruction is modeled as a multichannel joint sparse reconstruction problem. After the model is simplified by data interpolation, an efficient model-solving method is proposed by combining the three-dimensional fast Fourier transform, conjugate gradient method, and Newton iteration method, where the polarization scattering matrix and three-dimensional information of the target can be obtained at the same time. The proposed method ensures that the sparse support sets of different polarization channels and dimensions are consistent and utilizes the additional information generated by the correlation between data. On the basis of the simulation and electromagnetic calculation data, the experimental results indicate that the proposed method is tolerant of noise and immune to the types of targets. Moreover, the proposed method can effectively obtain the multidimensional reconstruction results of the target, where both the resolution of the imaging results and the estimation accuracy of the polarization scattering matrix are high. Independent processing of each polarization channel and three-dimensional multistage imaging ignore the correlation between data, resulting in the mismatch between scattering centers and the inaccurate acquisition of polarization scattering matrices. To address these issues, a full-polarization Synthetic Aperture Radar (SAR) joint multidimensional reconstruction method based on sparse reconstruction is proposed in this study. In this method, all polarization channels and dimensions are integrated by setting the joint sparse constraints, and the full-polarization SAR joint multidimensional reconstruction is modeled as a multichannel joint sparse reconstruction problem. After the model is simplified by data interpolation, an efficient model-solving method is proposed by combining the three-dimensional fast Fourier transform, conjugate gradient method, and Newton iteration method, where the polarization scattering matrix and three-dimensional information of the target can be obtained at the same time. The proposed method ensures that the sparse support sets of different polarization channels and dimensions are consistent and utilizes the additional information generated by the correlation between data. On the basis of the simulation and electromagnetic calculation data, the experimental results indicate that the proposed method is tolerant of noise and immune to the types of targets. Moreover, the proposed method can effectively obtain the multidimensional reconstruction results of the target, where both the resolution of the imaging results and the estimation accuracy of the polarization scattering matrix are high.
An azimuth multi-channel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multi-channel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multi-channel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multi-channel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multi-channel SAR. The effectiveness of the presented method is validated using multi-channel SAR simulation data. An azimuth multi-channel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multi-channel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multi-channel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multi-channel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multi-channel SAR. The effectiveness of the presented method is validated using multi-channel SAR simulation data.
Sea–land segmentation is a basic step in coastline extraction and nearshore target detection. Because of poor segmentation accuracy and complicated parameter adjustment, the traditional sea–land segmentation algorithm is difficult to adapt in practical applications. Convolutional neural networks, which can extract multiple hierarchical features of images, can be used as an alternative technical approach for sea–land segmentation tasks. Among them, BiSeNet exhibits good performance in the semantic segmentation of natural scene images and effectively balances segmentation accuracy and speed. However, for the sea–land segmentation of SAR images, BiSeNet cannot extract the contextual semantic and spatial information of SAR images; thus, the segmentation effect is poor. To address the aforementioned problem, this study reduced the number of convolution layers in the spatial path to reduce the loss of spatial information and selected the ResNet18 lightweight model as the backbone network for the context path to reduce the overfitting phenomenon and provide a broad receptive field. At the same time, strategies for edge enhancement and loss function are proposed to improve the segmentation performance of the network in the land and sea boundary region. Experimental results based on GF3 data showed that the proposed method effectively improves the prediction accuracy and segmentation rate of the network. The segmentation accuracy and F1 score of the proposed method are 0.9889 and 0.9915, respectively, and the processing rate of SAR image slices with the resolution of 1024 × 1024 is 12.7 frames/s, which are better than those of other state-of-the-art approaches. Moreover, the size of the network is more than half of that of BiSeNet and smaller than that of U-Net. Thus, the network exhibits strong generalization performance. Sea–land segmentation is a basic step in coastline extraction and nearshore target detection. Because of poor segmentation accuracy and complicated parameter adjustment, the traditional sea–land segmentation algorithm is difficult to adapt in practical applications. Convolutional neural networks, which can extract multiple hierarchical features of images, can be used as an alternative technical approach for sea–land segmentation tasks. Among them, BiSeNet exhibits good performance in the semantic segmentation of natural scene images and effectively balances segmentation accuracy and speed. However, for the sea–land segmentation of SAR images, BiSeNet cannot extract the contextual semantic and spatial information of SAR images; thus, the segmentation effect is poor. To address the aforementioned problem, this study reduced the number of convolution layers in the spatial path to reduce the loss of spatial information and selected the ResNet18 lightweight model as the backbone network for the context path to reduce the overfitting phenomenon and provide a broad receptive field. At the same time, strategies for edge enhancement and loss function are proposed to improve the segmentation performance of the network in the land and sea boundary region. Experimental results based on GF3 data showed that the proposed method effectively improves the prediction accuracy and segmentation rate of the network. The segmentation accuracy and F1 score of the proposed method are 0.9889 and 0.9915, respectively, and the processing rate of SAR image slices with the resolution of 1024 × 1024 is 12.7 frames/s, which are better than those of other state-of-the-art approaches. Moreover, the size of the network is more than half of that of BiSeNet and smaller than that of U-Net. Thus, the network exhibits strong generalization performance.
Suppression position of the traditional noise convolution modulation Synthetic Aperture Radar (SAR) jamming lags behind in range and suppression area in azimuth is uncontrollable. Considering this defect, an enhanced jamming method is proposed herein. First, the frequency of the intercepted signal is shifted in fast-time to control the suppression position in range. Then, the convolution with the noise is implemented, which has been filtered in slow-time, to control the suppression area in azimuth. Theoretical analysis and simulation results demonstrate that the proposed jamming method can efficiently control the jamming position in range and suppression area when compared with the traditional noise convolution modulation jamming. Even if some reconnaissance errors exist, the local scenario can still be shielded effectively. Furthermore, the utilization efficiency of jamming energy is also improved under the same condition, which will provide some reference values and inputs for engineering applications. Suppression position of the traditional noise convolution modulation Synthetic Aperture Radar (SAR) jamming lags behind in range and suppression area in azimuth is uncontrollable. Considering this defect, an enhanced jamming method is proposed herein. First, the frequency of the intercepted signal is shifted in fast-time to control the suppression position in range. Then, the convolution with the noise is implemented, which has been filtered in slow-time, to control the suppression area in azimuth. Theoretical analysis and simulation results demonstrate that the proposed jamming method can efficiently control the jamming position in range and suppression area when compared with the traditional noise convolution modulation jamming. Even if some reconnaissance errors exist, the local scenario can still be shielded effectively. Furthermore, the utilization efficiency of jamming energy is also improved under the same condition, which will provide some reference values and inputs for engineering applications.
Papers
Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm. Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm.
To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original Two-Stage Weighted Least Squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound (CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error. To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original Two-Stage Weighted Least Squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound (CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error.
The Very Low Frequency (VLF) signal of 10 kHz has strong penetrability of ground objects. Because of the antenna size, its application is limited. Therefore, it is important to study the VLF signal generation method based on appropriately sized high frequency radar antennas. The concept of generating VLF signal with high frequency array antenna is proposed in this paper. The waveform of the emission signal, staggered array structure design, and array parameter selection methods are presented and discussed. The pulse width of the composite signal is increased by using periodic pulse signals as radiation element signals. The resting period of the pulse signals is filled with the pulse width expansion generated by the array and the VLF signal with continuous time is composed in the target area. The performance of the composite VLF signal and the energy utilization of the emission signal are evaluated using Peak SideLobe Ratio (PSLR), Integrated SideLobe Ratio (ISLR) and through the spectrum comparison between the emission signal and the composite signal. With the 10 kHz VLF signal composed by 100 MHz radiant element signals, the hundred meter array is simulated. When the staggered array is constituted by nine arrays and the pulse width of the radiation element is set to 0.115 μs, PSLR and ISLR of the composite signal spectrum are –13.34 dB and –9.44 dB respectively, and the energy proportion of 10 kHz low-frequency signal in the composite signal is 89.79%. The effects of radiation element spacing error, time error, phase error and amplitude error of the radiation element signal and the target’s deviation are analyzed. It is found that the proposed method is an effective one and the simulation results have illustrated the effectiveness. The Very Low Frequency (VLF) signal of 10 kHz has strong penetrability of ground objects. Because of the antenna size, its application is limited. Therefore, it is important to study the VLF signal generation method based on appropriately sized high frequency radar antennas. The concept of generating VLF signal with high frequency array antenna is proposed in this paper. The waveform of the emission signal, staggered array structure design, and array parameter selection methods are presented and discussed. The pulse width of the composite signal is increased by using periodic pulse signals as radiation element signals. The resting period of the pulse signals is filled with the pulse width expansion generated by the array and the VLF signal with continuous time is composed in the target area. The performance of the composite VLF signal and the energy utilization of the emission signal are evaluated using Peak SideLobe Ratio (PSLR), Integrated SideLobe Ratio (ISLR) and through the spectrum comparison between the emission signal and the composite signal. With the 10 kHz VLF signal composed by 100 MHz radiant element signals, the hundred meter array is simulated. When the staggered array is constituted by nine arrays and the pulse width of the radiation element is set to 0.115 μs, PSLR and ISLR of the composite signal spectrum are –13.34 dB and –9.44 dB respectively, and the energy proportion of 10 kHz low-frequency signal in the composite signal is 89.79%. The effects of radiation element spacing error, time error, phase error and amplitude error of the radiation element signal and the target’s deviation are analyzed. It is found that the proposed method is an effective one and the simulation results have illustrated the effectiveness.